| Abbreviation | Expanded Form |
| Adam | Adaptive Moment Estimation |
| AN | Actual Non-Stressed Crop |
| ANN | Artificial Neural Network |
| ANOVA | Analysis of Variance |
| CIAE | Central Institute of Agricultural Engineering |
| CNN | Convolution Neural Network |
| DAS | Day After Sowing |
| DCNN | Deep Convolution Neural Network |
| DL | Deep Learning |
| DL-LSTM | Deep Learning-Long Short Term Memory |
| ETc | Evapotranspiration |
| F1 | F1 Score |
| ICAR | Indian Council of Agricultural Research |
| KNN | Kernel Nearest Neighbor |
| LR | Logistic Regression |
| LSTM | Long Short Term Memory |
| MAE | Mean Absolute Error |
| ML | Machine Learning |
| P | Precision |
| PS | Correctly Predicted Stressed Crop From all the predictions |
| RBF | Radial Basis Function |
| RF | Random Forest |
| RGB | Red Green Blue |
| RH | Relative Humidity |
| RWC | Relative Water Content |
| SD | Standard Deviation |
| Se | Sensitivity |
| Sgdm | Stochastic Gradient Descent with Momentum |
| SMC | Soil Moisture Content |
| S | Specificity |
| VM | Support Vector Machine |
| Ta | Air Temperature |
| Tc | Canopy Temperature |
| TE1 | Type 1 Error |
| TE2 | Type 2 Error |
| TN | True Negative |
| TS | True Positive |
| UAS | Unmanned Aerial System |